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題名 雲端運算環境下基於知識本體之資訊檢索系統建置-以半導體產業為例
Constructing ontology-based information retrieval system in cloud computing environment – the case of semiconductor industry作者 李佳穎
Li, Chia Ying貢獻者 劉文卿
Liou, Wen Ching
李佳穎
Li, Chia Ying關鍵詞 雲端運算
文字探勘
知識本體
資訊檢索
Cloud Computing
Text Mining
Ontology
Information Retrieval日期 2010 上傳時間 4-Sep-2013 16:58:18 (UTC+8) 摘要 本研究針對半導體產業,提供一智慧型搜尋功能,讓使用者在大量資料中能快速及準確地搜尋。為達此目的,本研究中定義知識空間及其組成元素,並發展一組程式以產生該知識空間及知識空間搜尋機制,以提升使用者生產力。所使用到的技術包含:(1)建立知識本體,(2)計算兩詞彙同時出現頻率,(3)計算詞彙與文件關聯度,(4)發展知識空間搜尋環境。
This study aims to provide an intelligent searching environment which users can search quickly and precisely from a large number of documents in semiconductor industry. In order to achieve the purpose, this paper defines a knowledge space and its composition elements to describe the knowledge of real world, and then develops a program to shorten the searching cost by providing the searching mechanism based on knowledge space. The techniques used in this study includes:(1) Construct 「Semiconductor Industry Ontology」(2) Compute the frequency of two terms appearing simultaneously (3) Compute the interrelatedness between terms and documents (4) Develop searching environment based on knowledge space.參考文獻 一、英文參考文獻[1] Agrawal, R., Srikant, R. (1994). Fast algorithms for mining association rules. IBM Research Report RJ9839. IBM Almaden Research Center.[2] Cecchet, E., Singh, R., Sharma,U., Shenoy, P. (2011). Dolly: virtualization-driven database provisioning for the cloud.[3] Chang, F., Dean, J., Ghemawat, S., Wilson, C. H., Deborah, A. W., Burrows. M., Chandra, T., Fikes, A., Robert, E. G. (2006). Bigtable: A Distributed Storage System for Structured Data. Seventh Symposium on Operating System Design and Implementation. Seattle, WA.[4] Dawei, S., Guiran, C., Chunguang, T., Xingwei, W. (2011). Enhancing Security by System-Level Virtualization in Cloud Computing Environments.[5] Dean, J., Ghemawat. S. (2004). MapReduce: Simplified Data Processing on Large Clusters. Proceedings of the 6th Symposium on Operating Systems Design and Implementatio. San Francisco, CA.[6] Gantz, J., Reinsel, D. (2009). As the Economy Contracts, the Digital Universe Expands. IDC Multimedia White Paper. Sponsored by EMC Corporation.[7] Gantz, J., Reinsel, D. (2010). A Digital Universe Decade – Are you Ready? IDC Multimedia White Paper. Sponsored by EMC Corporation.[8] Ghemawat, S., Gobioff, H., Shun-Tak Leung. (2003). The Google File System, 19th ACM Symposium on Operating Systems Principles. Lake George, NY.[9] Goldberg, R. P. (1973). Architectural Principles for Virtual Computer Systems. Harvard University. pp. 22-26.[10] Luhn, H.P. (1958). The Automatic Creation of Literature Abstracts. IBM Journal of Research and Development. Vol. 2, pp.157-165.[11] Maedche, A., Staab, S. (2001). Ontology Learning for the Semantic Web Export. IEEE Intelligent Systems In Intelligent Systems. IEEE, Vol. 16, No. 2., pp. 72-79.[12] Mell, P., Grance, T. (2009a). Effectively and Securely Using the Cloud Computing Paradigm, NIST Information Technology Laboratory.[13] Mell, P., Grance, T. (2009b). The NIST Definition of Cloud Computing, NIST Information Technology Laboratory.[14] Noy, N. F., McGuinness, D. L. (2001). Ontology development 101: A guide to creating your first ontology. Technical Report SMI-2001-0880, Stanford Medical Informatics.[15] Sullivan, D. (2001). Document Warehousing and Text Mining: Techniques for Improving Business Operations, Marketing, and Sales. New York, NY: John Wiley & Sons, Inc.[16] Tulloch, M. (2010). Understanding Microsoft Virtualization Solutions. Redmond, Washington:Microsoft Press. 二、中文參考文獻[17] 黃國政(2006)。運用文字探勘技術於人才招募推薦系統之研究。靜宜大學資訊管理研究所碩士論文。[18] 許偉忠(2007)。以成本敏感分類分析法建構之多語言文件分類技術。國立清華大學科技管理研究所碩士論文。[19] 朱明中(2010)。Windows Azure教戰手札。台北市:碁峰資訊股份有限公司。[20] 陳垂呈(2008)。利用分類分析發掘產品最適性之項目組合。南台科技大學資訊管理研究所碩士論文。[21] 陳瀅(2010)。雲端策略:雲端運算與虛擬化技術。台北市:天下雜誌股份有限公司。[22] 曾元顯(1997)。關鍵詞自動擷取技術與相關詞回饋,中國圖書館學會會報 ,第59 期,1997,59-64。[23] 蘇晏譁(2009)。運用文字探勘技術建置知識本體之研究-以財經文件為例。國立政治大學資訊管理研究所碩士論文。[24] 葉乃菁、王玳琪、張嘉珍、吳騏、賴志遠(2009)。建構創新政策研究工具--文字探勘之應用簡介,國研科技,第1期,2009,17-20。[25] 巫啟台(2002)。文件之關聯資訊萃取及其概念圖自動建構。國立成功大學資訊工程研究所碩士論文。三、網路參考資料[26] Amazon (2011a). Amazon EC2. Retrieved from:http://aws.amazon.com/ec2/[27] Amazon (2011b). Amazon S3. Retrieved from:http://aws.amazon.com/s3/[28] Garner (2008). Gartner Says Cloud Computing Will Be As Influential As E-business. Retrieved from:http://www.gartner.com/it/page.jsp?id=707508 [29] IBM (2007). IBM Introduces Ready-to-Use Cloud Computing. Retrieved from:http://www-03.ibm.com/press/us/en/pressrelease/22613.wss[30] Google (2011)。Google應用服務引擎。取自:http://code.google.com/intl/zh-TW/appengine/[31] Salesforce (2011)。Force.com產品介紹。取自:http://www.salesforce.com/tw/platform/products.jsp。[32] Wikipedia (2007)。雲端運算。取自:http://bit.ly/ok1ZlG[33] Windows Azure (2011)。Windows Azure Platform產品簡介。取自:http://www.microsoft.com/taiwan/windowsazure/[34] 趨勢科技(2011)。趨勢科技防毒技術。取自:http://www.trendmicro.com.tw/spn/index.asp[35] 謝良奇(2008)。HP、Intel、Yahoo共組開放源碼雲端運算計畫。取自:http://bit.ly/916Pcy 描述 碩士
國立政治大學
資訊管理研究所
98356003
99資料來源 http://thesis.lib.nccu.edu.tw/record/#G0098356003 資料類型 thesis dc.contributor.advisor 劉文卿 zh_TW dc.contributor.advisor Liou, Wen Ching en_US dc.contributor.author (Authors) 李佳穎 zh_TW dc.contributor.author (Authors) Li, Chia Ying en_US dc.creator (作者) 李佳穎 zh_TW dc.creator (作者) Li, Chia Ying en_US dc.date (日期) 2010 en_US dc.date.accessioned 4-Sep-2013 16:58:18 (UTC+8) - dc.date.available 4-Sep-2013 16:58:18 (UTC+8) - dc.date.issued (上傳時間) 4-Sep-2013 16:58:18 (UTC+8) - dc.identifier (Other Identifiers) G0098356003 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/60211 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊管理研究所 zh_TW dc.description (描述) 98356003 zh_TW dc.description (描述) 99 zh_TW dc.description.abstract (摘要) 本研究針對半導體產業,提供一智慧型搜尋功能,讓使用者在大量資料中能快速及準確地搜尋。為達此目的,本研究中定義知識空間及其組成元素,並發展一組程式以產生該知識空間及知識空間搜尋機制,以提升使用者生產力。所使用到的技術包含:(1)建立知識本體,(2)計算兩詞彙同時出現頻率,(3)計算詞彙與文件關聯度,(4)發展知識空間搜尋環境。 zh_TW dc.description.abstract (摘要) This study aims to provide an intelligent searching environment which users can search quickly and precisely from a large number of documents in semiconductor industry. In order to achieve the purpose, this paper defines a knowledge space and its composition elements to describe the knowledge of real world, and then develops a program to shorten the searching cost by providing the searching mechanism based on knowledge space. The techniques used in this study includes:(1) Construct 「Semiconductor Industry Ontology」(2) Compute the frequency of two terms appearing simultaneously (3) Compute the interrelatedness between terms and documents (4) Develop searching environment based on knowledge space. en_US dc.description.tableofcontents 致謝 I摘要 IIAbstract III目錄 IV表目錄 VI圖目錄 VII第一章 緒論 1第一節 研究背景與動機 1第二節 研究目的 2第三節 研究架構 4第二章 文獻探討 6第一節 雲端運算 6第二節 本體論 10第三節 文字探勘 12一、定義與技術 12二、關鍵詞萃取 13第三章 研究方法與系統設計 16第一節 研究步驟 16一、半導體產業概念模型 19二、知識元素統計 23三、資訊檢索 24第二節 系統架構 28第四章 雛型系統實作 30第一節 雛型系統實作工具 30第二節 雛型系統實作架構 31一、虛擬化 31二、服務導向架構 33三、三層式系統架構 36第三節 雛型系統實作流程 37一、建立知識本體 37二、設定資料來源與資料處理 38三、建立本體索引 39四、統計知識元素 39五、實作檢索介面 40第四節 雛型系統操作情境 42第五章 結論與建議 47第一節 結論 47第二節 建議 47參考文獻 49一、英文參考文獻 49二、中文參考文獻 51三、網路參考資料 52附錄一 53附錄二 54 zh_TW dc.format.extent 2471126 bytes - dc.format.mimetype application/pdf - dc.language.iso en_US - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0098356003 en_US dc.subject (關鍵詞) 雲端運算 zh_TW dc.subject (關鍵詞) 文字探勘 zh_TW dc.subject (關鍵詞) 知識本體 zh_TW dc.subject (關鍵詞) 資訊檢索 zh_TW dc.subject (關鍵詞) Cloud Computing en_US dc.subject (關鍵詞) Text Mining en_US dc.subject (關鍵詞) Ontology en_US dc.subject (關鍵詞) Information Retrieval en_US dc.title (題名) 雲端運算環境下基於知識本體之資訊檢索系統建置-以半導體產業為例 zh_TW dc.title (題名) Constructing ontology-based information retrieval system in cloud computing environment – the case of semiconductor industry en_US dc.type (資料類型) thesis en dc.relation.reference (參考文獻) 一、英文參考文獻[1] Agrawal, R., Srikant, R. (1994). Fast algorithms for mining association rules. IBM Research Report RJ9839. IBM Almaden Research Center.[2] Cecchet, E., Singh, R., Sharma,U., Shenoy, P. (2011). Dolly: virtualization-driven database provisioning for the cloud.[3] Chang, F., Dean, J., Ghemawat, S., Wilson, C. H., Deborah, A. W., Burrows. M., Chandra, T., Fikes, A., Robert, E. G. (2006). Bigtable: A Distributed Storage System for Structured Data. Seventh Symposium on Operating System Design and Implementation. Seattle, WA.[4] Dawei, S., Guiran, C., Chunguang, T., Xingwei, W. (2011). Enhancing Security by System-Level Virtualization in Cloud Computing Environments.[5] Dean, J., Ghemawat. S. (2004). MapReduce: Simplified Data Processing on Large Clusters. Proceedings of the 6th Symposium on Operating Systems Design and Implementatio. San Francisco, CA.[6] Gantz, J., Reinsel, D. (2009). As the Economy Contracts, the Digital Universe Expands. IDC Multimedia White Paper. Sponsored by EMC Corporation.[7] Gantz, J., Reinsel, D. (2010). A Digital Universe Decade – Are you Ready? IDC Multimedia White Paper. Sponsored by EMC Corporation.[8] Ghemawat, S., Gobioff, H., Shun-Tak Leung. (2003). The Google File System, 19th ACM Symposium on Operating Systems Principles. Lake George, NY.[9] Goldberg, R. P. (1973). Architectural Principles for Virtual Computer Systems. Harvard University. pp. 22-26.[10] Luhn, H.P. (1958). The Automatic Creation of Literature Abstracts. IBM Journal of Research and Development. Vol. 2, pp.157-165.[11] Maedche, A., Staab, S. (2001). Ontology Learning for the Semantic Web Export. IEEE Intelligent Systems In Intelligent Systems. IEEE, Vol. 16, No. 2., pp. 72-79.[12] Mell, P., Grance, T. (2009a). Effectively and Securely Using the Cloud Computing Paradigm, NIST Information Technology Laboratory.[13] Mell, P., Grance, T. (2009b). The NIST Definition of Cloud Computing, NIST Information Technology Laboratory.[14] Noy, N. F., McGuinness, D. L. (2001). Ontology development 101: A guide to creating your first ontology. Technical Report SMI-2001-0880, Stanford Medical Informatics.[15] Sullivan, D. (2001). Document Warehousing and Text Mining: Techniques for Improving Business Operations, Marketing, and Sales. New York, NY: John Wiley & Sons, Inc.[16] Tulloch, M. (2010). Understanding Microsoft Virtualization Solutions. Redmond, Washington:Microsoft Press. 二、中文參考文獻[17] 黃國政(2006)。運用文字探勘技術於人才招募推薦系統之研究。靜宜大學資訊管理研究所碩士論文。[18] 許偉忠(2007)。以成本敏感分類分析法建構之多語言文件分類技術。國立清華大學科技管理研究所碩士論文。[19] 朱明中(2010)。Windows Azure教戰手札。台北市:碁峰資訊股份有限公司。[20] 陳垂呈(2008)。利用分類分析發掘產品最適性之項目組合。南台科技大學資訊管理研究所碩士論文。[21] 陳瀅(2010)。雲端策略:雲端運算與虛擬化技術。台北市:天下雜誌股份有限公司。[22] 曾元顯(1997)。關鍵詞自動擷取技術與相關詞回饋,中國圖書館學會會報 ,第59 期,1997,59-64。[23] 蘇晏譁(2009)。運用文字探勘技術建置知識本體之研究-以財經文件為例。國立政治大學資訊管理研究所碩士論文。[24] 葉乃菁、王玳琪、張嘉珍、吳騏、賴志遠(2009)。建構創新政策研究工具--文字探勘之應用簡介,國研科技,第1期,2009,17-20。[25] 巫啟台(2002)。文件之關聯資訊萃取及其概念圖自動建構。國立成功大學資訊工程研究所碩士論文。三、網路參考資料[26] Amazon (2011a). Amazon EC2. Retrieved from:http://aws.amazon.com/ec2/[27] Amazon (2011b). Amazon S3. Retrieved from:http://aws.amazon.com/s3/[28] Garner (2008). Gartner Says Cloud Computing Will Be As Influential As E-business. Retrieved from:http://www.gartner.com/it/page.jsp?id=707508 [29] IBM (2007). IBM Introduces Ready-to-Use Cloud Computing. Retrieved from:http://www-03.ibm.com/press/us/en/pressrelease/22613.wss[30] Google (2011)。Google應用服務引擎。取自:http://code.google.com/intl/zh-TW/appengine/[31] Salesforce (2011)。Force.com產品介紹。取自:http://www.salesforce.com/tw/platform/products.jsp。[32] Wikipedia (2007)。雲端運算。取自:http://bit.ly/ok1ZlG[33] Windows Azure (2011)。Windows Azure Platform產品簡介。取自:http://www.microsoft.com/taiwan/windowsazure/[34] 趨勢科技(2011)。趨勢科技防毒技術。取自:http://www.trendmicro.com.tw/spn/index.asp[35] 謝良奇(2008)。HP、Intel、Yahoo共組開放源碼雲端運算計畫。取自:http://bit.ly/916Pcy zh_TW